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by io.github.pipeworx-io

Polymarket Edge Tracker

polymarket_edge_tracker
Read-onlyIdempotent

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage), not intraday.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback in days (default 14, clamp 2-30).
windowNoWhich polymarket_edges window family to read snapshots for: 24hr | 1wk | 1mo (default 1wk).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added
  2. Removed
  3. Added

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds context: data comes from daily snapshots, has a 60-day TTL, and decay is computed from daily closes (not intraday). It also details the response structure (tracked, expired, snapshot_dates). This adds significant behavioral clarity beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but well-organized: it starts with the core purpose, then explains output format, and ends with limitations. Every sentence adds value, though some could be trimmed. It is front-loaded with the most important information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description compensates by thoroughly explaining the response structure (tracked, expired, snapshot_dates arrays with their fields). It also covers limitations like TTL and decay computation. For a tool with two optional parameters, this is highly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers both parameters with descriptions (100% coverage). The description adds minor extra context: default values (days=14, window='1wk') and explains that 'window' refers to snapshot family. This is helpful but the schema already provides adequate meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it provides edge persistence and decay telemetry, answering the specific question of how long an edge has existed and whether it is shrinking. It distinguishes itself from the sibling 'polymarket_edges' by focusing on historical time-series analysis rather than just current edges.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly tells when to use this tool (to evaluate edge persistence vs. recency) by contrasting 'fresh wide edge' with '3-week-old wide edge'. It does not explicitly state when not to use it or list direct alternatives, but the contrast provides adequate guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation2/5

Several tools are nearly indistinguishable without deep reading: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same universal query, and the beta currently behaves identically. The prediction-market clister (polymarket_arbitrage, pollymarket_edges, pollymarket_edge_tracker, pollymarket_fill_risk, pollymarket_kalshi_spread) and value-estimation tools (attom_avm, attom_assessment, attom_rental_avm) have fuzzy boundaries that will cause misselection.

Naming Consistency4/5

The vast majority of tools follow a consistent lower_snake-case convention with clear prefixes (attom_*, polymarket_*, pipeworx_*) and verb-noun forms (generate_llms_txt, list_subscriptions, resolve_entity). Minor deviations exist like the bare memory verbs remember, recall, forget and domain-noun names entity_profile, bet_research, but the overall pattern is predictable and readable.

Tool Count2/5

39 tools is well above the comfortable 3-15 range and signals scope creep: the server bundles real-estate, prediction markets, company research, memory, subscriptions, web utilities, and a universal data router. Many of these could be grouped into a smaller number of composite tools, as the descriptions themselves already suggest (e.g. ask_pipeworx as the default entry point).

Completeness3/5

Coverage is deep for prediction markets, company financials, and real-estate, with complete memory and subscription lifecycles (remember/recall/forgeet, subscribe/list/unsubscribe/recent_alerts). However, many advertised domains (weather, clinical trials, news, government records) are only reachable through the generic ask_pipeworx router rather than dedicated tools, and scan_dependency is npm-only, leaving obvious gaps for other ecosystems.

Resources